The increasing popularity of social networks and users’ tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societally important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster-related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation and analyzing people’s sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming at a separate task. The presented analysis and the associated dataset, which is made public, will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.

The domain of Speech Emotion Recognition (SER) has experienced a tremendous revolution due to the outbreak of deep learning, which has contributed, as in many other research areas, to a significant boost in terms of model accuracy. SER refers to a branch of Human-Computer Interaction (HCI), which deals with recognizing emotional states from human speech. Although being a thriving field of research, SER still poses a number of non-trivial challenges, mainly due to the lack of shared best practices and high-quality datasets that can make the developed models suitable for their application in real environments. In this paper, we implement a CNN-based model combined with a Convolutional Attention Block, and conduct a series of experiments involving a selection of four English datasets popularly used for SER applications: RAVDESS, TESS, CREMA-D, and IEMOCAP. After testing the proposed pipeline on individual datasets, achieving a mean accuracy of 83%, 100%, 68% and 63% respectively, we perform an extensive cross-validation between common emotional classes belonging to single datasets or combinations of them, with the aim to investigate the generalization abilities of the extracted features.

In this paper, we propose a novel approach to automatically plan video cameras positioning in indoor environments for surveillance applications. In order to ensure maximum coverage of the observed scene, we have implemented an ad-hoc tool based on the particle swarm optimization. The camera is modeled as a 2D function. A Rayleigh distribution is used to characterize the relevance of the observed object with respect to the distance from the camera and a Gaussian distribution is adopted to model the horizontal field-of-view. Knowing the environment characteristics and the location of obstacles and walls, it is possible to derive a reliable positioning of the sensors. Results are first presented for very simple scenarios; the algorithm is then run to solve the problem in a more realistic environment model.

Crowd surveillance plays a key role to ensure safety and security in public areas. Surveillance systems traditionally rely on fixed camera networks, which suffer from limitations, as coverage of the monitored area, video resolution and analytic performance. On the other hand, a smart camera network provides the ability to reconfigure the sensing infrastructure by incorporating active devices such as pan-tilt-zoom (PTZ) cameras and UAV-based cameras, thus enabling the network to adapt over time to changes in the scene. We propose a new decentralised approach for network reconfiguration, where each camera dynamically adapts its parameters and position to optimise scene coverage. Two policies for decentralised camera reconfiguration are presented: a greedy approach and a reinforcement learning approach. In both cases, cameras are able to locally control the state of their neighbourhood and dynamically adjust their position and PTZ parameters. When crowds are present, the network balances between global coverage of the entire scene and high resolution for the crowded areas. We evaluate our approach in a simulated environment monitored with fixed, PTZ and UAV-based cameras.

Camera calibration is a necessary preliminary step in computer vision for the estimation of the position of objects in the 3D world. Despite the intrinsic camera parameters can be easily computed offline, extrinsic parameters need to be computed each time a camera changes its position, thus not allowing for fast and dynamic network re-configuration. In this paper we present an unsupervised and automatic framework for the estimation of the extrinsic parameters of a camera network, which leverages on optimised 3D human mesh recovery from a single image, and which does not require the use of additional markers. We show how it is possible to retrieve the real-world position of the cameras in the network together with the floor plane, exploiting regular RGB images and with a weak prior knowledge of the internal parameters. Our framework can also work with a single camera and in real-time, allowing the user to add, re-position, or remove cameras from the network in a dynamic fashion.

In this work we present a framework for physical rehabilitation, which is based on hand tracking. One particular requirement in physical rehabilitation is the capability of the patient to correctly reproduce a specific path, following an example provided by the medical staff. Currently, these assignments are typically performed manually, and a nurse or doctor, who supervises the correctness of the movement, constantly assists the patient throughout the whole rehabilitation process. With the proposed system, our aim is to provide medical institutions and patients with a low-cost and portable instrument to automatically assess the rehabilitation improvements. To evaluate the performance of the exercise, and to determine the distance between the trial and the reference path, we adopted the dynamic time warping (DTW) and the longest common sub-sequence (LCSS) as discriminating metrics. Trajectories and numerical values are then stored to track the history of the patient and appraise the improvements of the rehabilitation process over time. Thanks to the tests conducted with real patients, it has been possible to evaluate the quality of the proposed tool, in terms of both graphical interface and functionalities.